Comments (3)
So, the way it is implemented now was tailored to Frank Moosman's data. These data are all 64x870, which seems to give a good tradeoff between speed and precision. The way I handle multiple points that fall into the same pixel is that only the furthest reading is taken per pixel, but all the points that fall into it get assigned the same label. It can create some minor artifacts, but seemed to work well in practice for me.
You can however, pick any resolution you want if you look into the ProjectionParams
class by adding another function like this one with any resolution you want.
Be advised, that working with KITTI data directly might be a little imprecise as the HDL64 has two sets of beams that do not exactly match onto a single depth image.
from depth_clustering.
i think KITTI should be set as:
params.SetSpan(SpanParams(-180_deg, 180_deg, 4000),
SpanParams::Direction::HORIZONTAL);
because Velodyne HDL-64E has 0.09 degree angular resolution.
from depth_clustering.
- I also think HDL64 row step should be 4000 or 2048, but will this increase runtime a lot ?
- can you explain further about "HDL64 has two sets of beams that do not exactly match onto a single depth image"
- the AngleDiff::PreComputeAlphaVecs, if the angle is add by step, then it's a constant value, why need a vector to store and obtain it by r,c indices ?
thanks for your work and reply!
from depth_clustering.
Related Issues (20)
- visualisation of clusters HOT 2
- Implementation on velodyne lidar HOT 5
- How to cluster your own point cloud data and visualize HOT 1
- Status of this package HOT 1
- assertion on unaligned arrays HOT 1
- Release for ROS Noetic HOT 4
- Can't achieve the same effect with my own data HOT 1
- Query on Cluster Visualization HOT 2
- Feature Request: Support ROS pointclouds from Ouster 128-beam LIDAR HOT 1
- Using two 16 line radar fusion data as input HOT 1
- build error HOT 7
- running in real time HOT 1
- running in real time HOT 3
- subscribing to clusters
- compile err error: ‘boost::filesystem’ has not been declared
- Opencv verison
- kitti dataset test error HOT 3
- Get labeled point cloud object HOT 2
- Interesting issue when using ProjectionParams::FromConfigFile method HOT 1
- 3 MEMS combined pointcloud HOT 1
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